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Record W4399518709 · doi:10.1088/2057-1976/ad567d

Lung sparing in MR-guided non-adaptive SBRT treatment of peripheral lung tumors

2024· article· en· W4399518709 on OpenAlexaff
Ho Young Lee, Grace Lee, D. Ferguson, Shu‐Hui Hsu, Yue‐Houng Hu, Elizabeth Huynh, Atchar Sudhyadhom, Christopher L. Williams, D.N. Cagney, Kelly Fitzgerald, Benjamin H. Kann, David Kozono, Jonathan E. Leeman, Raymond H. Mak, Zhaohui Han

Bibliographic record

VenueBiomedical Physics & Engineering Express · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsMedicineLungNuclear medicineLung cancerLung volumesRadiation treatment planningRadiation therapyRadiologyMagnetic resonance imagingInternal medicine

Abstract

fetched live from OpenAlex

Abstract Objective. We aim to: (1) quantify the benefits of lung sparing using non-adaptive magnetic resonance guided stereotactic body radiotherapy (MRgSBRT) with advanced motion management for peripheral lung cancers compared to conventional x-ray guided SBRT (ConvSBRT); (2) establish a practical decision-making guidance metric to assist a clinician in selecting the appropriate treatment modality. Approach. Eleven patients with peripheral lung cancer who underwent breath-hold, gated MRgSBRT on an MR-guided linear accelerator (MR linac) were studied. Four-dimensional computed tomography (4DCT)-based retrospective planning using an internal target volume (ITV) was performed to simulate ConvSBRT, which were evaluated against the original MRgSBRT plans. Metrics analyzed included planning target volume (PTV) coverage, various lung metrics and the generalized equivalent unform dose (gEUD). A dosimetric predictor for achievable lung metrics was derived to assist future patient triage across modalities. Main results. PTV coverage was high (median V100% > 98%) and comparable for both modalities. MRgSBRT had significantly lower lung doses as measured by V20 (median 3.2% versus 4.2%), mean lung dose (median 3.3 Gy versus 3.8 Gy) and gEUD. Breath-hold, gated MRgSBRT resulted in an average reduction of 47% in PTV volume and an average increase of 19% in lung volume. Strong correlation existed between lung metrics and the ratio of PTV to lung volumes (R PTV/Lungs ) for both modalities, indicating that R PTV/Lungs may serve as a good predictor for achievable lung metrics without the need for pre-planning. A threshold value of R PTV/Lungs < 0.035 is suggested to achieve V20 < 10% using ConvSBRT. MRgSBRT should otherwise be considered if the threshold cannot be met. Significance. The benefits of lung sparing using MRgSBRT were quantified for peripheral lung tumors; R PTV/Lungs was found to be an effective predictor for achievable lung metrics across modalities. R PTV/Lungs can assist a clinician in selecting the appropriate modality without the need for labor-intensive pre-planning, which has significant practical benefit for a busy clinic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.271
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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